A novel statistical analysis and interpretation of flow cytometry data

被引:10
|
作者
Banks, H. T. [1 ,2 ]
Kapraun, D. F. [1 ,2 ]
Thompson, W. Clayton [1 ,2 ]
Peligero, Cristina [3 ]
Argilaguet, Jordi [3 ]
Meyerhans, Andreas [3 ]
机构
[1] N Carolina State Univ, Ctr Res Sci Computat, Raleigh, NC 27695 USA
[2] N Carolina State Univ, Ctr Quantitat Sci Biomed, Raleigh, NC 27695 USA
[3] Univ Pompeu Fabra, Dept Expt & Hlth Sci, ICREA Infect Biol Lab, Barcelona 08003, Spain
基金
美国国家科学基金会;
关键词
immunology; flow cytometry; cyton models; mathematical and statistical models; label dynamics; parameter estimation; cellular models; MEASURING LYMPHOCYTE-PROLIFERATION; IN-VITRO; DIVISION; MODEL; RESPONSES; DYNAMICS; VIVO;
D O I
10.1080/17513758.2013.812753
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.
引用
收藏
页码:96 / 132
页数:37
相关论文
共 50 条
  • [1] Statistical file matching of flow cytometry data
    Lee, Gyemin
    Finn, William
    Scott, Clayton
    JOURNAL OF BIOMEDICAL INFORMATICS, 2011, 44 (04) : 663 - 676
  • [2] Flow Cytometry Data Analysis
    Yildiz, Eyyup
    Ensari, Tolga
    Sener, Leyla Turker
    2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
  • [3] BCyto: A shiny app for flow cytometry data analysis
    Bonilha, Caio Santos
    MOLECULAR AND CELLULAR PROBES, 2022, 65
  • [4] Computational Analysis of Microbial Flow Cytometry Data
    Rubbens, Peter
    Props, Ruben
    MSYSTEMS, 2021, 6 (01)
  • [5] Analysis of Clinical Flow Cytometric Immunophenotyping Data by Clustering on Statistical Manifolds: Treating Flow Cytometry Data as High-Dimensional Objects
    Finn, William G.
    Carter, Kevin M.
    Raich, Raviv
    Stoolman, Lloyd M.
    Hero, Alfred O.
    CYTOMETRY PART B-CLINICAL CYTOMETRY, 2009, 76B (01) : 1 - 7
  • [6] COUPLED TENSOR FACTORIZATION FOR FLOW CYTOMETRY DATA ANALYSIS
    Flores, Philippe
    Harle, Guillaume
    Notarantonio, Anne-Beatrice
    Usevich, Konstantin
    D'Aveni, Maud
    Grandemange, Stephanie
    Rubio, Marie-Therese
    Brie, David
    2022 IEEE 32ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP), 2022,
  • [7] Statistical criteria to establish optimal antibody dilution in flow cytometry analysis
    Collino, Cesar J. G.
    Jaldin-Fincati, Javier R.
    Chiabrando, Gustavo A.
    CYTOMETRY PART B-CLINICAL CYTOMETRY, 2007, 72B (03) : 223 - 226
  • [8] FLOPTICS: A Novel Automated Gating Technique for Flow Cytometry Data
    Sriphum, Wiwat
    Wills, Gary
    Green, Nicolas G.
    PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON COMPLEXITY, FUTURE INFORMATION SYSTEMS AND RISK (COMPLEXIS), 2020, : 96 - 102
  • [9] Flow cytometry data analysis: Recent tools and algorithms
    Montante, Sebastiano
    Brinkman, Ryan R.
    INTERNATIONAL JOURNAL OF LABORATORY HEMATOLOGY, 2019, 41 : 56 - 62
  • [10] Information Preserving Component Analysis: Data Projections for Flow Cytometry Analysis
    Carter, Kevin M.
    Raich, Raviv
    Finn, William G.
    Hero, Alfred O., III
    IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2009, 3 (01) : 148 - 158